Algorithm Design: Foundations, Analysis And Internet Examples

Are you looking for something different in your Algorithms text? Are you looking for an Algorithms text that offers theoretical analysis techniques as well as design patterns and experimental methods for the engineering of algorithms? Michael Goodrich and Roberto Tamassia, authors of the successful, Data Structures and Algorithms in Java, 2/e, have written Algorithm Design, a text designed to provide a comprehensive introduction to the design, implementation and analysis of computer algorithms and data structures from a modern perspective.


Written for an undergraduate, junior-senior algorithms course this text offers several implementation case studies and uses Internet applications to motivate many topics such as hashing, sorting and searching.

* Algorithmic design patterns are presented to provide a clear approach for implementation algorithms.

* Internet Algorithms. Are you interested in improving student interest in the study of algorithms? Algorithms Design encourages the learning of traditional algorithm topics now finding new applications in the Internet, as well as topics of specific interest to companies and research labs working on Internet applications.

* Authors Goodrich and Tamassia replace complex average-case analysis of sophisticated data structures with intuitive analysis of simple data structures and algorithms. Use of randomization will allow instructors to avoid the need for motivating complex analyses with assumptions about input distributions.

* Strong website support offers algorithm animations. Tutorials provide intuition and increased understanding of algorithms.

* Data structures, code examples provide concrete examples of Java implementations.


* Do your students respond to visual learning tools? Goodrich and Tamassia make use of visual proofs to help students better understand algebraic and induction proofs.

PART I: FUNDAMENTAL TOOLS.

Algorithm Analysis.

Basic Data Structures.

Search Trees and Skip Lists.

Sorting, Sets, and Selection.

Fundamental Techniques.

PART II: GRAPH ALGORITHMS.

Graphs.

Weighted Graphs.

Network Flow and Matching.

PART III: INTERNET ALGORITHMICS.

Text Processing.

Number Theory and Cryptograhy.

Network Algorithms.

PART IV: ADDITIONAL TOPICS.

Computational Geometry.

NP-Completeness.

Algorithmic Frameworks.

Appendix: Useful Mathematical Facts.

Bibliography.

Index.